{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><center><h1 style=\"font-size:4em;color:#2467C0\">Week 1: Soccer Data Analysis</h1></center>\n",
    "<br>\n",
    "<table>\n",
    "<col width=\"550\">\n",
    "<col width=\"450\">\n",
    "<tr>\n",
    "<td><img src=\"http://www.foodculture.dk/~/media/foodculture/_arkiv/aktuelt/2012/14/fodbold.jpg?w=700\" align=\"middle\" style=\"width:550px;height:360px;\"/></td>\n",
    "<td>\n",
    "This week, we will be using an open dataset from the popular site <a href=\"https://www.kaggle.com\">Kaggle</a>. This <a href=\"https://www.kaggle.com/hugomathien/soccer\">European Soccer Database</a> has more than 25,000 matches and more than 10,000 players for European professional soccer seasons from 2008 to 2016. \n",
    "<br>\n",
    "<br>\n",
    "Although we won’t be getting into the details of it for our example, the dataset even has attributes on weekly game updates, team line up, and detailed match events.\n",
    "<br>\n",
    "<br>\n",
    "The goal of this notebook is to walk you through an end to end process of analyzing a dataset and introduce you to what we will be covering in this course. Our simple analytical process will include some steps for exploring  and cleaning our dataset, some steps for predicting player performance using basic statistics, and some steps for grouping similar clusters using machine learning. \n",
    "<br>\n",
    "<br>\n",
    "Let's get started with our Python journey!\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "<br> To get started, we will need to:\n",
    "<ol>\n",
    "<li>Download the data from: <a href=\"https://www.kaggle.com/hugomathien/soccer\">https://www.kaggle.com/hugomathien/soccer</a></li>\n",
    "<li>Extract the zip file called \"soccer.zip\"</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "<br> We will start by importing the Python libraries we will be using in this analysis. These libraries include:\n",
    "<ul>\n",
    "<li><b>sqllite3</b> for interacting with a local relational database; and</li>\n",
    "<li><b>pandas</b> and <b>numpy</b> for data ingestion and manipulation.</li>\n",
    "<li><b>matplotlib</b> for data visualization</li>\n",
    "<li>specific methods from <b>sklearn</b> for Machine Learning and \n",
    "<li><b>customplot</b>, which contains custom functions we have written for this notebook</li>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ingest Data\n",
    "\n",
    "Now, we will need to read the dataset using the commands below. \n",
    "\n",
    "<b>Note:</b> Make sure you run the import cell above (shift+enter) before you run the data ingest code below.\n",
    "\n",
    "<b>df</b> is a variable pointing to a pandas data frame. We will learn about them in an upcoming week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create your connection.\n",
    "cnx = sqlite3.connect('database.sqlite')\n",
    "df = pd.read_sql_query(\"SELECT * FROM Player_Attributes\", cnx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Exploring Data</h1>\n",
    "\n",
    "We will start our data exploration by generating simple statistics of the data. \n",
    "<br><br> \n",
    "Let us look at what the data columns are using a pandas attribute called \"columns\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'player_fifa_api_id', 'player_api_id', 'date', 'overall_rating',\n",
       "       'potential', 'preferred_foot', 'attacking_work_rate',\n",
       "       'defensive_work_rate', 'crossing', 'finishing', 'heading_accuracy',\n",
       "       'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',\n",
       "       'long_passing', 'ball_control', 'acceleration', 'sprint_speed',\n",
       "       'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',\n",
       "       'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',\n",
       "       'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',\n",
       "       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',\n",
       "       'gk_reflexes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next will display simple statistics of our dataset. You need to run each cell to make sure you see the outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>183978.0</td>\n",
       "      <td>91989.500000</td>\n",
       "      <td>53110.018250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45995.25</td>\n",
       "      <td>91989.5</td>\n",
       "      <td>137983.75</td>\n",
       "      <td>183978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <td>183978.0</td>\n",
       "      <td>165671.524291</td>\n",
       "      <td>53851.094769</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155798.00</td>\n",
       "      <td>183488.0</td>\n",
       "      <td>199848.00</td>\n",
       "      <td>234141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_api_id</th>\n",
       "      <td>183978.0</td>\n",
       "      <td>135900.617324</td>\n",
       "      <td>136927.840510</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>34763.00</td>\n",
       "      <td>77741.0</td>\n",
       "      <td>191080.00</td>\n",
       "      <td>750584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall_rating</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>68.600015</td>\n",
       "      <td>7.041139</td>\n",
       "      <td>33.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>73.460353</td>\n",
       "      <td>6.592271</td>\n",
       "      <td>39.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>74.0</td>\n",
       "      <td>78.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crossing</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>55.086883</td>\n",
       "      <td>17.242135</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finishing</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>49.921078</td>\n",
       "      <td>19.038705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>53.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heading_accuracy</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>57.266023</td>\n",
       "      <td>16.488905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short_passing</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>62.429672</td>\n",
       "      <td>14.194068</td>\n",
       "      <td>3.0</td>\n",
       "      <td>57.00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volleys</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>49.468436</td>\n",
       "      <td>18.256618</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dribbling</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>59.175154</td>\n",
       "      <td>17.744688</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curve</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>52.965675</td>\n",
       "      <td>18.255788</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free_kick_accuracy</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>49.380950</td>\n",
       "      <td>17.831746</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_passing</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>57.069880</td>\n",
       "      <td>14.394464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball_control</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>63.388879</td>\n",
       "      <td>15.196671</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>67.659357</td>\n",
       "      <td>12.983326</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sprint_speed</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>68.051244</td>\n",
       "      <td>12.569721</td>\n",
       "      <td>12.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agility</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>65.970910</td>\n",
       "      <td>12.954585</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reactions</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>66.103706</td>\n",
       "      <td>9.155408</td>\n",
       "      <td>17.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>65.189496</td>\n",
       "      <td>13.063188</td>\n",
       "      <td>12.0</td>\n",
       "      <td>58.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_power</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>61.808427</td>\n",
       "      <td>16.135143</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jumping</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>66.969045</td>\n",
       "      <td>11.006734</td>\n",
       "      <td>14.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stamina</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>67.038544</td>\n",
       "      <td>13.165262</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strength</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>67.424529</td>\n",
       "      <td>12.072280</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_shots</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>53.339431</td>\n",
       "      <td>18.367025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aggression</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>60.948046</td>\n",
       "      <td>16.089521</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interceptions</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>52.009271</td>\n",
       "      <td>19.450133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positioning</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>55.786504</td>\n",
       "      <td>18.448292</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>57.873550</td>\n",
       "      <td>15.144086</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>penalties</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>55.003986</td>\n",
       "      <td>15.546519</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marking</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>46.772242</td>\n",
       "      <td>21.227667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standing_tackle</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>50.351257</td>\n",
       "      <td>21.483706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sliding_tackle</th>\n",
       "      <td>181265.0</td>\n",
       "      <td>48.001462</td>\n",
       "      <td>21.598778</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>53.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gk_diving</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>14.704393</td>\n",
       "      <td>16.865467</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gk_handling</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>16.063612</td>\n",
       "      <td>15.867382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gk_kicking</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>20.998362</td>\n",
       "      <td>21.452980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gk_positioning</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>16.132154</td>\n",
       "      <td>16.099175</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gk_reflexes</th>\n",
       "      <td>183142.0</td>\n",
       "      <td>16.441439</td>\n",
       "      <td>17.198155</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count           mean            std     min        25%  \\\n",
       "id                  183978.0   91989.500000   53110.018250     1.0   45995.25   \n",
       "player_fifa_api_id  183978.0  165671.524291   53851.094769     2.0  155798.00   \n",
       "player_api_id       183978.0  135900.617324  136927.840510  2625.0   34763.00   \n",
       "overall_rating      183142.0      68.600015       7.041139    33.0      64.00   \n",
       "potential           183142.0      73.460353       6.592271    39.0      69.00   \n",
       "crossing            183142.0      55.086883      17.242135     1.0      45.00   \n",
       "finishing           183142.0      49.921078      19.038705     1.0      34.00   \n",
       "heading_accuracy    183142.0      57.266023      16.488905     1.0      49.00   \n",
       "short_passing       183142.0      62.429672      14.194068     3.0      57.00   \n",
       "volleys             181265.0      49.468436      18.256618     1.0      35.00   \n",
       "dribbling           183142.0      59.175154      17.744688     1.0      52.00   \n",
       "curve               181265.0      52.965675      18.255788     2.0      41.00   \n",
       "free_kick_accuracy  183142.0      49.380950      17.831746     1.0      36.00   \n",
       "long_passing        183142.0      57.069880      14.394464     3.0      49.00   \n",
       "ball_control        183142.0      63.388879      15.196671     5.0      58.00   \n",
       "acceleration        183142.0      67.659357      12.983326    10.0      61.00   \n",
       "sprint_speed        183142.0      68.051244      12.569721    12.0      62.00   \n",
       "agility             181265.0      65.970910      12.954585    11.0      58.00   \n",
       "reactions           183142.0      66.103706       9.155408    17.0      61.00   \n",
       "balance             181265.0      65.189496      13.063188    12.0      58.00   \n",
       "shot_power          183142.0      61.808427      16.135143     2.0      54.00   \n",
       "jumping             181265.0      66.969045      11.006734    14.0      60.00   \n",
       "stamina             183142.0      67.038544      13.165262    10.0      61.00   \n",
       "strength            183142.0      67.424529      12.072280    10.0      60.00   \n",
       "long_shots          183142.0      53.339431      18.367025     1.0      41.00   \n",
       "aggression          183142.0      60.948046      16.089521     6.0      51.00   \n",
       "interceptions       183142.0      52.009271      19.450133     1.0      34.00   \n",
       "positioning         183142.0      55.786504      18.448292     2.0      45.00   \n",
       "vision              181265.0      57.873550      15.144086     1.0      49.00   \n",
       "penalties           183142.0      55.003986      15.546519     2.0      45.00   \n",
       "marking             183142.0      46.772242      21.227667     1.0      25.00   \n",
       "standing_tackle     183142.0      50.351257      21.483706     1.0      29.00   \n",
       "sliding_tackle      181265.0      48.001462      21.598778     2.0      25.00   \n",
       "gk_diving           183142.0      14.704393      16.865467     1.0       7.00   \n",
       "gk_handling         183142.0      16.063612      15.867382     1.0       8.00   \n",
       "gk_kicking          183142.0      20.998362      21.452980     1.0       8.00   \n",
       "gk_positioning      183142.0      16.132154      16.099175     1.0       8.00   \n",
       "gk_reflexes         183142.0      16.441439      17.198155     1.0       8.00   \n",
       "\n",
       "                         50%        75%       max  \n",
       "id                   91989.5  137983.75  183978.0  \n",
       "player_fifa_api_id  183488.0  199848.00  234141.0  \n",
       "player_api_id        77741.0  191080.00  750584.0  \n",
       "overall_rating          69.0      73.00      94.0  \n",
       "potential               74.0      78.00      97.0  \n",
       "crossing                59.0      68.00      95.0  \n",
       "finishing               53.0      65.00      97.0  \n",
       "heading_accuracy        60.0      68.00      98.0  \n",
       "short_passing           65.0      72.00      97.0  \n",
       "volleys                 52.0      64.00      93.0  \n",
       "dribbling               64.0      72.00      97.0  \n",
       "curve                   56.0      67.00      94.0  \n",
       "free_kick_accuracy      50.0      63.00      97.0  \n",
       "long_passing            59.0      67.00      97.0  \n",
       "ball_control            67.0      73.00      97.0  \n",
       "acceleration            69.0      77.00      97.0  \n",
       "sprint_speed            69.0      77.00      97.0  \n",
       "agility                 68.0      75.00      96.0  \n",
       "reactions               67.0      72.00      96.0  \n",
       "balance                 67.0      74.00      96.0  \n",
       "shot_power              65.0      73.00      97.0  \n",
       "jumping                 68.0      74.00      96.0  \n",
       "stamina                 69.0      76.00      96.0  \n",
       "strength                69.0      76.00      96.0  \n",
       "long_shots              58.0      67.00      96.0  \n",
       "aggression              64.0      73.00      97.0  \n",
       "interceptions           57.0      68.00      96.0  \n",
       "positioning             60.0      69.00      96.0  \n",
       "vision                  60.0      69.00      97.0  \n",
       "penalties               57.0      67.00      96.0  \n",
       "marking                 50.0      66.00      96.0  \n",
       "standing_tackle         56.0      69.00      95.0  \n",
       "sliding_tackle          53.0      67.00      95.0  \n",
       "gk_diving               10.0      13.00      94.0  \n",
       "gk_handling             11.0      15.00      93.0  \n",
       "gk_kicking              12.0      15.00      97.0  \n",
       "gk_positioning          11.0      15.00      96.0  \n",
       "gk_reflexes             11.0      15.00      96.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Data Cleaning: Handling Missing Data</h1>\n",
    "Real data is never clean. We need to make sure we clean the data by converting or getting rid of null or missing values.<br>\n",
    "The next code cell will show you if any of the 183978 rows have null value in one of the 42 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, (183978, 42))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#is any row NULL ?\n",
    "df.isnull().any().any(), df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to find how many data points in each column are null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        0\n",
       "player_fifa_api_id        0\n",
       "player_api_id             0\n",
       "date                      0\n",
       "overall_rating          836\n",
       "potential               836\n",
       "preferred_foot          836\n",
       "attacking_work_rate    3230\n",
       "defensive_work_rate     836\n",
       "crossing                836\n",
       "finishing               836\n",
       "heading_accuracy        836\n",
       "short_passing           836\n",
       "volleys                2713\n",
       "dribbling               836\n",
       "curve                  2713\n",
       "free_kick_accuracy      836\n",
       "long_passing            836\n",
       "ball_control            836\n",
       "acceleration            836\n",
       "sprint_speed            836\n",
       "agility                2713\n",
       "reactions               836\n",
       "balance                2713\n",
       "shot_power              836\n",
       "jumping                2713\n",
       "stamina                 836\n",
       "strength                836\n",
       "long_shots              836\n",
       "aggression              836\n",
       "interceptions           836\n",
       "positioning             836\n",
       "vision                 2713\n",
       "penalties               836\n",
       "marking                 836\n",
       "standing_tackle         836\n",
       "sliding_tackle         2713\n",
       "gk_diving               836\n",
       "gk_handling             836\n",
       "gk_kicking              836\n",
       "gk_positioning          836\n",
       "gk_reflexes             836\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing Null Values by Deleting Them\n",
    "\n",
    "In our next two lines, we will drop the null values by going through each row.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix it\n",
    "\n",
    "# Take initial # of rows\n",
    "rows = df.shape[0]\n",
    "\n",
    "# Drop the NULL rows\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we check the null values and number of rows, we will see that there are no null values and number of rows decreased accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183978\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(False, (180354, 42))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if all NULLS are gone ?\n",
    "print(rows)\n",
    "df.isnull().any().any(), df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find exactly how many lines we removed, we need to subtract the current number of rows in our data frame from the original number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3624"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many rows with NULL values?\n",
    "\n",
    "rows - df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data table has many lines as you have seen. We can only look at few lines at once. Instead of looking at same top 10 lines every time, we shuffle - so we get to see different random sample on top. This way, we make sure the data is not in any particular order when we try sampling from it (like taking top or bottom few rows) by randomly shuffling the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shuffle the rows of df so we get a distributed sample when we display top few rows\n",
    "\n",
    "df = df.reindex(np.random.permutation(df.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Predicting: 'overall_rating' of a player</h1>\n",
    "Now that our data cleaning step is reasonably complete and we can trust and understand the data more, we will start diving into the dataset further. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's take a look at top few rows.\n",
    "\n",
    "We will use the head function for data frames for this task. This gives us every column in every row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>...</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70358</th>\n",
       "      <td>70359</td>\n",
       "      <td>153185</td>\n",
       "      <td>18501</td>\n",
       "      <td>2013-05-24 00:00:00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>right</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157136</th>\n",
       "      <td>157137</td>\n",
       "      <td>9676</td>\n",
       "      <td>33639</td>\n",
       "      <td>2014-02-28 00:00:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88350</th>\n",
       "      <td>88351</td>\n",
       "      <td>171791</td>\n",
       "      <td>26552</td>\n",
       "      <td>2013-05-24 00:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93660</th>\n",
       "      <td>93661</td>\n",
       "      <td>190499</td>\n",
       "      <td>231093</td>\n",
       "      <td>2016-01-14 00:00:00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>left</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142706</th>\n",
       "      <td>142707</td>\n",
       "      <td>137671</td>\n",
       "      <td>25550</td>\n",
       "      <td>2015-10-02 00:00:00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>right</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  player_fifa_api_id  player_api_id                 date  \\\n",
       "70358    70359              153185          18501  2013-05-24 00:00:00   \n",
       "157136  157137                9676          33639  2014-02-28 00:00:00   \n",
       "88350    88351              171791          26552  2013-05-24 00:00:00   \n",
       "93660    93661              190499         231093  2016-01-14 00:00:00   \n",
       "142706  142707              137671          25550  2015-10-02 00:00:00   \n",
       "\n",
       "        overall_rating  potential preferred_foot attacking_work_rate  \\\n",
       "70358             69.0       72.0          right                 low   \n",
       "157136            82.0       82.0          right              medium   \n",
       "88350             73.0       73.0          right              medium   \n",
       "93660             69.0       73.0           left              medium   \n",
       "142706            75.0       75.0          right                high   \n",
       "\n",
       "       defensive_work_rate  crossing     ...       vision  penalties  marking  \\\n",
       "70358               medium      44.0     ...         55.0       41.0     68.0   \n",
       "157136              medium      69.0     ...         74.0       85.0     20.0   \n",
       "88350               medium      25.0     ...         54.0       42.0     69.0   \n",
       "93660               medium      60.0     ...         68.0       66.0     20.0   \n",
       "142706                 low      69.0     ...         64.0       60.0     37.0   \n",
       "\n",
       "        standing_tackle  sliding_tackle  gk_diving  gk_handling  gk_kicking  \\\n",
       "70358              75.0            76.0       13.0         10.0         6.0   \n",
       "157136             39.0            35.0        7.0         10.0        11.0   \n",
       "88350              74.0            73.0        6.0         10.0        15.0   \n",
       "93660              34.0            28.0        7.0         12.0        14.0   \n",
       "142706             32.0            22.0       15.0          7.0        15.0   \n",
       "\n",
       "        gk_positioning  gk_reflexes  \n",
       "70358             12.0         10.0  \n",
       "157136            13.0         12.0  \n",
       "88350              9.0          5.0  \n",
       "93660             10.0         14.0  \n",
       "142706             8.0         16.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the time, we are only interested in plotting some columns. In that case, we can use the pandas column selection option as follows. Please ignore the first column in the output of the one line code below. It is the unique identifier that acts as an index for the data.<br><br>\n",
    "<b>Note:</b> From this point on, we will start referring to the columns as \"features\" in our description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>penalties</th>\n",
       "      <th>overall_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70358</th>\n",
       "      <td>41.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157136</th>\n",
       "      <td>85.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88350</th>\n",
       "      <td>42.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93660</th>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142706</th>\n",
       "      <td>60.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24248</th>\n",
       "      <td>70.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96598</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112810</th>\n",
       "      <td>36.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136795</th>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27988</th>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        penalties  overall_rating\n",
       "70358        41.0            69.0\n",
       "157136       85.0            82.0\n",
       "88350        42.0            73.0\n",
       "93660        66.0            69.0\n",
       "142706       60.0            75.0\n",
       "24248        70.0            81.0\n",
       "96598        74.0            72.0\n",
       "112810       36.0            53.0\n",
       "136795       67.0            66.0\n",
       "27988        40.0            60.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10][['penalties', 'overall_rating']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Correlation Analysis \n",
    "Next, we will check if 'penalties' is correlated to 'overall_rating'. We are using a similar selection operation, bu this time for all the rows and within the correlation function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Are these correlated (using Pearson's correlation coefficient) ?\n",
    "\n",
    "df['overall_rating'].corr(df['penalties'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Pearson's Correlation Coefficient for these two columns is 0.39. <br><br>\n",
    "Pearson goes from -1 to +1. A value of 0 would have told there is no correlation, so we shouldn’t bother looking at that attribute. A value of 0.39 shows some correlation, although it could be stronger. <br><br>\n",
    "At least, we have these attributes which are slightly correlated. This gives us hope that we might be able to build a meaningful predictor using these ‘weakly’ correlated features.<br><br>\n",
    "Next, we will create a list of features that we would like to iterate the same operation on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a list of potential Features that you want to measure correlation with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "potentialFeatures = ['acceleration', 'curve', 'free_kick_accuracy', 'ball_control', 'shot_power', 'stamina']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The for loop below prints out the correlation coefficient of \"overall_rating\" of a player with each feature we added to the list as potential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acceleration: 0.243998\n",
      "curve: 0.357566\n",
      "free_kick_accuracy: 0.349800\n",
      "ball_control: 0.443991\n",
      "shot_power: 0.428053\n",
      "stamina: 0.325606\n"
     ]
    }
   ],
   "source": [
    "# check how the features are correlated with the overall ratings\n",
    "\n",
    "for f in potentialFeatures:\n",
    "    related = df['overall_rating'].corr(df[f])\n",
    "    print(\"%s: %f\" % (f,related))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which features have the highest correlation with overall_rating?\n",
    "\n",
    "Looking at the values printed by the previous cell, we notice that the to two are \"ball_control\" (0.44) and \"shot_power\" (0.43). So these two features seem to have higher correlation with \"overall_rating\".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Data Visualization:</h1>\n",
    "Next we will start plotting the correlation coefficients of each feature with \"overall_rating\". We start by selecting the columns and creating a list with correlation coefficients, called \"correlations\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "overall_rating        1.000000\n",
       "reactions             0.771856\n",
       "potential             0.765435\n",
       "short_passing         0.458243\n",
       "ball_control          0.443991\n",
       "long_passing          0.434525\n",
       "vision                0.431493\n",
       "shot_power            0.428053\n",
       "penalties             0.392715\n",
       "long_shots            0.392668\n",
       "positioning           0.368978\n",
       "volleys               0.361739\n",
       "curve                 0.357566\n",
       "crossing              0.357320\n",
       "dribbling             0.354191\n",
       "free_kick_accuracy    0.349800\n",
       "finishing             0.330079\n",
       "stamina               0.325606\n",
       "aggression            0.322782\n",
       "strength              0.315684\n",
       "heading_accuracy      0.313324\n",
       "jumping               0.258978\n",
       "sprint_speed          0.253048\n",
       "interceptions         0.249094\n",
       "acceleration          0.243998\n",
       "agility               0.239963\n",
       "standing_tackle       0.163986\n",
       "balance               0.160211\n",
       "marking               0.132185\n",
       "sliding_tackle        0.128054\n",
       "gk_kicking            0.028799\n",
       "gk_diving             0.027675\n",
       "gk_positioning        0.008029\n",
       "gk_reflexes           0.007804\n",
       "gk_handling           0.006717\n",
       "id                   -0.003738\n",
       "player_fifa_api_id   -0.278703\n",
       "player_api_id        -0.328315\n",
       "Name: overall_rating, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix[\"overall_rating\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['potential',  'crossing', 'finishing', 'heading_accuracy',\n",
    "       'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',\n",
    "       'long_passing', 'ball_control', 'acceleration', 'sprint_speed',\n",
    "       'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',\n",
    "       'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',\n",
    "       'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',\n",
    "       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',\n",
    "       'gk_reflexes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list containing Pearson's correlation between 'overall_rating' with each column in cols\n",
    "correlations = [ df['overall_rating'].corr(df[f]) for f in cols ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 34)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cols), len(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make sure that the number of selected features and the correlations calculated are the same, e.g., both 34 in this case. Next couple of cells show some lines of code that use pandas plaotting functions to create a 2D graph of these correlation vealues and column names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function for plotting a dataframe with string columns and numeric values\n",
    "\n",
    "def plot_dataframe(df, y_label):  \n",
    "    color='coral'\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(20, 12)\n",
    "    plt.ylabel(y_label)\n",
    "\n",
    "    ax = df.correlation.plot(linewidth=3.3, color=color)\n",
    "    ax.set_xticks(df.index)\n",
    "    ax.set_xticklabels(df.attributes, rotation=75); #Notice the ; (remove it and see what happens !)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe using cols and correlations\n",
    "\n",
    "df2 = pd.DataFrame({'attributes': cols, 'correlation': correlations})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jSZKGWLgNAABQBARJeVdaGr7u783uHgAAv2W5I0livA0AAKAgCJLyLtJI6pXK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\nkCAJQDz1VpEEJE/5INvKdvYV0s4NNlBa+6J9kypJp18U7f4A9MwduN1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0vVJLc493157N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\nh4yS15Fy/jJKm65MUTOz36FMnAnIuGyN7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c6T9uh8J6TfqXoSnx94wlQWMDotIF3RLjdN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chgMvFE/Xa15BENSGcCQFbZm5zewnwBPufoaZ3QhMdfdxECJ/QctI2UgAryHNrX2Aw9Jjq6PM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\nIAiC4GsRjqQgCIIgCIIgCIIgCILga/H/cfpS1Ny5tacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11c68a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot above dataframe using the function we created\n",
    "    \n",
    "plot_dataframe(df2, 'Player\\'s Overall Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:1.5em;color:#FB41C4\">Analysis of Findings</h1>\n",
    "\n",
    "Now it is time for you to analyze what we plotted. Suppose you have to predict a player's overall rating. Which 5 player attributes would you ask for?\n",
    "<br><br>\n",
    "<b>Hint:</b> Which are the five features with highest correlation coefficients?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Clustering Players into Similar Groups</h1>\n",
    "\n",
    "Until now, we used basic statistics and correlation coefficients to start forming an opinion, but can we do better? What if we took some features and start looking at each player using those features? Can we group similar players based on these features? Let's see how we can do this. \n",
    "\n",
    "<b>Note:</b> Generally, someone with domain knowledge needs to define which features. We could have also selected some of the features with highest correlation with overall_rating. However, it does not guarantee best outcome always as we are not sure if the top five features are independent. For example, if 4 of the 5 features depend on the remaining 1 feature, taking all 5 does not give new information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Features on Which to Group Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gk_kicking', 'potential', 'marking', 'interceptions', 'standing_tackle']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features you want to use for grouping players\n",
    "\n",
    "select5features = ['gk_kicking', 'potential', 'marking', 'interceptions', 'standing_tackle']\n",
    "select5features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a new dataframe by selecting the features you just defined\n",
    "\n",
    "df_select = df[select5features].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>potential</th>\n",
       "      <th>marking</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>standing_tackle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70358</th>\n",
       "      <td>6.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157136</th>\n",
       "      <td>11.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88350</th>\n",
       "      <td>15.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93660</th>\n",
       "      <td>14.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142706</th>\n",
       "      <td>15.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gk_kicking  potential  marking  interceptions  standing_tackle\n",
       "70358          6.0       72.0     68.0           64.0             75.0\n",
       "157136        11.0       82.0     20.0           39.0             39.0\n",
       "88350         15.0       73.0     69.0           73.0             74.0\n",
       "93660         14.0       73.0     20.0           20.0             34.0\n",
       "142706        15.0       75.0     37.0           33.0             32.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_select.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform KMeans Clustering\n",
    "\n",
    "Now we will use a machine learning method called KMeans to cluster the values (i.e., player features on gk_kicking, potential, marking, interceptions, and standing_tackle). We will ask for four clusters. We will talk about KMeans clustering and other machine learning tools in Python in Week 7 so we won't discuss these methods here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform scaling on the dataframe containing the features\n",
    "\n",
    "data = scale(df_select)\n",
    "\n",
    "# Define number of clusters\n",
    "noOfClusters = 4\n",
    "\n",
    "# Train a model\n",
    "model = KMeans(init='k-means++', n_clusters=noOfClusters, n_init=20).fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________\n",
      "\n",
      "Count of players in each cluster\n",
      "__________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    50553\n",
       "1    55891\n",
       "2    50119\n",
       "3    23791\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(90*'_')\n",
    "print(\"\\nCount of players in each cluster\")\n",
    "print(90*'_')\n",
    "\n",
    "pd.value_counts(model.labels_, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_centers(featuresUsed, centers):\n",
    "    from itertools import cycle, islice\n",
    "    from pandas.tools.plotting import parallel_coordinates\n",
    "    import matplotlib.pyplot as plt\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    \n",
    "    colNames = list(featuresUsed)\n",
    "    colNames.append('prediction')\n",
    "    # Zip with a column called 'prediction' (index)\n",
    "    Z = [np.append(A, index) for index, A in enumerate(centers)]\n",
    "    \n",
    "    # Convert to pandas for plotting\n",
    "    P = pd.DataFrame(Z, columns=colNames)\n",
    "    P['prediction'] = P['prediction'].astype(int)\n",
    "    return P\n",
    "\n",
    "def parallel_plot(data):\n",
    "    from itertools import cycle, islice\n",
    "    from pandas.tools.plotting import parallel_coordinates\n",
    "    import matplotlib.pyplot as plt\n",
    "    \n",
    "    my_colors = list(islice(cycle(['b', 'r', 'g', 'y', 'k']), None, len(data)))\n",
    "    plt.figure(figsize=(15,8)).gca().axes.set_ylim([-2.5,+2.5])\n",
    "    parallel_coordinates(data, 'prediction', color = my_colors, marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>potential</th>\n",
       "      <th>marking</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.044069</td>\n",
       "      <td>0.702462</td>\n",
       "      <td>1.027478</td>\n",
       "      <td>0.982509</td>\n",
       "      <td>1.030224</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.477134</td>\n",
       "      <td>0.106217</td>\n",
       "      <td>-0.947702</td>\n",
       "      <td>-0.975290</td>\n",
       "      <td>-0.914228</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.335691</td>\n",
       "      <td>-0.846052</td>\n",
       "      <td>0.547593</td>\n",
       "      <td>0.405864</td>\n",
       "      <td>0.549872</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.920642</td>\n",
       "      <td>0.038723</td>\n",
       "      <td>-1.110320</td>\n",
       "      <td>-0.651676</td>\n",
       "      <td>-1.199555</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gk_kicking  potential   marking  interceptions  standing_tackle  prediction\n",
       "0   -0.044069   0.702462  1.027478       0.982509         1.030224           0\n",
       "1   -0.477134   0.106217 -0.947702      -0.975290        -0.914228           1\n",
       "2   -0.335691  -0.846052  0.547593       0.405864         0.549872           2\n",
       "3    1.920642   0.038723 -1.110320      -0.651676        -1.199555           3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a composite dataframe for plotting\n",
    "# ... Use custom function declared in customplot.py (which we imported at the beginning of this notebook)\n",
    "\n",
    "P = pd_centers(featuresUsed=select5features, centers=model.cluster_centers_)\n",
    "P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Visualization of Clusters</h1>\n",
    "We now have 4 clusters based on the features we selected, we can treat them as profiles for similar groups of players. We can visualize these profiles by plotting the centers for each cluster, i.e., the average values for each featuere within the cluster. We will use matplotlib for this visualization. We will learn more about matplotlib in Week 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For plotting the graph inside the notebook itself, we use the following command\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sli/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:25: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n"
     ]
    },
    {
     "data": {
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AAADHESpbISbmDKWmpisqaqK2bv2Btm+/UR5PhdNlAQAAAIBjCJWtFBraV6ecslJxcbdq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\n3z6+W5JM03y4ide0KFR6PNLatTX3R27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b9T9HnKusQM/nXiLzDZY6BPQV6hDgH6HSJ2OM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\nmW2f62tMrkpKDu8QNHNzR6e4xOgrhEqfCJWAN4mrv1bNrdKc4+YQKJE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11049fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_plot(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h1 style=\"font-size:1.5em;color:#FB41C4\">Analysis of Findings</h1>\n",
    "### Can you identify the groups for each of the below?\n",
    "\n",
    "<ul>\n",
    "<li>Two groups are very similar except in gk_kicking - these players can coach each other on gk_kicking, where they differ.</li>\n",
    "<li>Two groups are somewhat similar to each other except in potential.</li>\n",
    "</ul>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}
